R02: Identify which job roles are most affected by AI
RO3: Skills Demand Insights
Introduction
Welcome to our analysis on how AI adoption is transforming the global job market. This presentation focuses on identifying emerging skill demands and the influence of remote versus in-person work environments.
Problem Definition
Rapid AI integration is reshaping job roles and required skill sets. Organizations face challenges in workforce planning, upskilling, and aligning recruitment with evolving market needs.
Research Objectives
RO1: Assess the impact of AI adoption on overall job growth or decline.
RO2: Identify which job roles are most affected by AI and why.
RO3: Determine the most in-demand skills and examine how remote vs. in-person work influences skill requirements.
Importance of the Research
For Businesses: Guides strategic hiring and training programs.
For Employees: Clarifies skill development pathways for career resilience.
For Policymakers: Informs education and workforce policies to support an AI-driven economy.
Population & Sampling/Data Collection
All professional job roles (focus: UX/UI designers, project managers, marketing professionals) globally or in the U.S., across industries (tech, marketing, etc.), from 2020–2025.
The current convenience sample and pre-compiled data provide a starting point, but stratified random sampling and enhanced, diverse collection methods would strengthen reliability and address the topic’s significance.
Data Wrangling
# Clear environmentrm(list =ls())# Load librarieslibrary(readr)library(dplyr)# Load data (adjust path as needed)data <-read_csv("ai_job_market_insights.csv")# Data wranglingdata_clean <- data %>% dplyr::mutate(AI_Adoption_Level =factor(AI_Adoption_Level, levels =c("Low", "Medium", "High")),Job_Growth_Projection =factor(Job_Growth_Projection, levels =c("Decline", "Stable", "Growth")),Salary_USD =as.numeric(Salary_USD) ) %>% dplyr::filter(!is.na(Salary_USD) &!is.na(AI_Adoption_Level) &!is.na(Job_Growth_Projection))# Verifystr(data_clean)
# Read the CSV filelibrary(readr)ai_job <-read_csv("ai_job_market_insights.csv")# Inspect the data without X11head(ai_job) # first few rows
# A tibble: 6 × 12
Job_Title Industry Company_Size Location AI_Adoption_Level Automation_Risk
<chr> <chr> <chr> <chr> <chr> <chr>
1 Cybersecurit… Enterta… Small Dubai Medium High
2 Marketing Sp… Technol… Large Singapo… Medium High
3 AI Researcher Technol… Large Singapo… Medium High
4 Sales Manager Retail Small Berlin Low High
5 Cybersecurit… Enterta… Small Tokyo Low Low
6 UX Designer Educati… Large San Fra… Medium Medium
# ℹ 6 more variables: Required_Skills <chr>, Salary_USD <dbl>,
# Remote_Friendly <chr>, Job_Growth_Projection <chr>, Remote <chr>,
# Salary_Bin <chr>
Based on Demand Level and Automation Risk - Variables: Independent: AI_Adoption_Level (Low=1, Medium=2, High=3). Dependent: Automation_Risk (Low=1, Medium=2, High=3)
# Now we are going to create a risk score (High scores = HIgh risk) based on the Automation_Risk , it helps quantify the risk level for comparisonai_job <- ai_job %>%mutate(Automation_Risk_Score =case_when(Automation_Risk =="Low"~1, Automation_Risk =="Medium"~2, Automation_Risk =="High"~3 ))# Next, we will create a demand indicator based on Job_Growth_Projection , simplifies the growth project into a three tier demand scale ai_job <- ai_job %>%mutate(Demand_Level =case_when( Job_Growth_Projection =="Growth"~"High", Job_Growth_Projection =="Stable"~"Medium", Job_Growth_Projection =="Decline"~"Low" ))
Job Most Impacted by AI
Prompt: Identify which jobs are the most affected by AI
Approach: Analyze demand by job title, focusing on stable/growing roles.
Findings: Project Manager and AI Researcher show high/medium demand.Human-centric skills (e.g., communication) and specialized expertise (e.g., machine learning) resist automation.
Findings: Marketing Specialists ($93K, 60% remote) decline, while Project Managers ($92K, 55% remote) grow, suggesting skills drive demand over pay.
# Compare salary and remote work by demandpay_remote_demand <- ai_job %>%group_by(Demand_Level) %>%summarise(Avg_Salary =mean(Salary_USD),Remote_Percent =mean(Remote_Friendly =="Yes") *100,Job_Count =n() )print(pay_remote_demand)
# A tibble: 3 × 4
Demand_Level Avg_Salary Remote_Percent Job_Count
<chr> <dbl> <dbl> <int>
1 High 89975. 49.7 169
2 Low 91934. 48.5 169
3 Medium 91782. 52.5 162
Visualization
Salary by Demand
# Visualizeggplot(pay_remote_demand, aes(x = Demand_Level, y = Avg_Salary, fill = Remote_Percent)) +geom_bar(stat ="identity") +labs(title ="Average Salary by Demand Level with Remote Work Influence", x ="Demand Level", y ="Average Salary (USD)") +theme_minimal() +scale_fill_gradient(low ="lightblue", high ="darkblue")
RO2 Conclusions
Key Findings ::: incremental - High-Risk Jobs: UX Designers and Marketing Specialists face high automation risk in high-AI industries (e.g., Technology). - In-Demand Jobs: AI Researchers and Project Managers show stable/growing demand due to specialized and human-centric skills. - Pay/Remote Work: Higher salaries and remote options correlate with demand, but human judgment is critical. :::
RQ3 / H3
Research Question 3 (RQ3): What skills are most in demand in an AI-driven job market, and how does remote work versus in-person work affect these skill requirements?
Hypothesis 3 (H3): The prevalence of certain skills differs significantly between remote and in-person roles, with technical skills (e.g., Python, Cloud Architecture) being more common in remote positions and soft skills (e.g., Communication, Teamwork) more common in in-person roles.
Variables and Their Roles
Variable
Role
Description
Required_Skills
Dependent Variable (DV)
Count of each individual skill extracted from job listings
Remote
Independent Variable 1
Categorical: “Remote” vs. “In-Person”
Industry
Independent Variable 2
Categorical: sector of the job (e.g., Tech, Marketing)
Salary_Bin
Independent Variable 3
Categorical: “High Salary” vs. “Low Salary”
Operationalization of the Scale
Required_Skills: Each skill listed in Required_Skills is split into one row per skill; frequency counts measure demand.
Remote: Derived from Remote_Friendly column; binary factor.
Industry: Directly from Industry field; nominal categories.
Salary_Bin: Binned at the median Salary_USD; above = “High Salary”, below = “Low Salary”.
Data Wrangling
library(tidyverse)# Load raw datadf <-read_csv("ai_job_market_insights.csv")# Clean, split skills, and create flagsro3_data <- df %>%filter(!is.na(Required_Skills),!is.na(Remote_Friendly),!is.na(Industry),!is.na(Salary_USD) ) %>%separate_rows(Required_Skills, sep =",\\s*") %>%mutate(Remote =if_else(Remote_Friendly =="Yes", "Remote", "In-Person"),Salary_Bin =if_else( Salary_USD >median(Salary_USD, na.rm =TRUE),"High Salary", "Low Salary" ) )# Select top 5 most common skills overalltop5_skills <- ro3_data %>%count(Required_Skills, name ="Total") %>%slice_max(Total, n =5) %>%pull(Required_Skills)# Subset to top 5ro3_top5 <- ro3_data %>%filter(Required_Skills %in% top5_skills)remote_counts <- ro3_top5 %>%count(Required_Skills, Remote, name ="Count")industry_counts <- ro3_top5 %>%count(Required_Skills, Industry, name ="Count")salary_counts <- ro3_top5 %>%count(Required_Skills, Salary_Bin, name ="Count")# Display the full Remote vs In-Person tableknitr::kable( remote_counts,caption ="Top-5 Skills: Remote vs In-Person",align =c("l","l","r"))
Industry Differences: Tech roles favor Machine Learning and Data Analysis, while Marketing/Retail focus on SEO and Creative Suite.
Salary Tier: High-paying positions require advanced skills like AI Ethics and Cloud Architecture; lower-paying roles rely on MS Office and Basic SQL.
Conclusion
Key Findings
Remote roles favor technical skills: Python, Cloud Architecture, Cybersecurity and Machine Learning top the list for fully remote positions.
In-person roles emphasize soft skills: Communication, Teamwork and Project Management remain critical where on-site collaboration is required.
Industry and pay tiers diverge: Tech industries and high-salary jobs demand advanced AI-related expertise; lower-paid and non-tech roles rely on foundational tools like MS Office and basic SQL.
Implications for Businesses
Targeted upskilling: Invest in AI/data training for remote teams and soft-skill workshops for in-house staff.
Strategic hiring: Tailor job postings—highlight technical skills for remote, human-centric for on-site.
Flexible work models: Leverage remote policies to access niche talent while preserving on-site culture.
Workforce planning: Use these insights to forecast talent needs, align L&D budgets, and shape long-term recruitment strategies.
